The Diffusion of Predictive Analytics for Innovative Interactive Marketing Strategy in International E-Fashion Commerce

Authors

DOI:

https://doi.org/10.17010/ijom/2025/v55/i10/175612

Keywords:

predictive analytics, e-fashion, e-commerce, DOI theory, TOE framework.
Publication Chronology: Paper Submission Date : July 1, 2025 ; Paper sent back for Revision : July 25, 2025 ; Paper Acceptance Date : September 10, 2025 ; Paper Published Online : October 15, 2025

Abstract

Purpose : This research explored the factors prompting the diffusion of predictive analytics (PA) in international e-fashion companies. A typology of motives, advantages, and diffusion challenges was provided, and recommendations on overcoming them were suggested to PA executives.

Methodology : Based on integrating the technology – organization – environment (TOE) perspectives into the steps of diffusion of innovation (DOI) theory, a new systemic framework was developed to explain the process of diffusing PA in e-fashion and to help design interactive, innovative marketing strategies that enhanced the customer experience in such an international e-commerce industry. This study adopted an interpretative approach by conducting 20 semi-structured interviews with e-commerce analytics leaders in the top 10 fashion companies between 2020 and the end of 2024.

Findings : The findings referred to a range of motives based on intention to use, market niche, and informational, transactional, and transformational values provided by the companies. The challenges emphasized by the participants were complexity, organizational readiness, compatibility, management support, observability, privacy and security, external support, trialability, competitive pressure, and government regulations.

Practical Implications : The study offered guidelines for PAs on how to set the right questions, frame their dataset, and set the right interactive, innovative strategy for customers.

Originality : A dynamic framework of PA diffusion was provided to reflect the “Linear vs. Cyclical Process,” “Static Factors vs. Dynamic Processes,” “Inclusion of Post-Adoption Benefits,” “Nuanced Understanding of Management Support,” and “Specification of Key Challenges and Pathways.”

Downloads

Download data is not yet available.

Published

2025-10-08

How to Cite

Mohamad, M., Chowdhury, D., Ahmad, A., Jyawali, H., & Khan, A. (2025). The Diffusion of Predictive Analytics for Innovative Interactive Marketing Strategy in International E-Fashion Commerce. Indian Journal of Marketing, 55(10), 30–52. https://doi.org/10.17010/ijom/2025/v55/i10/175612

References

1) Ahmmed, M. M., Rahman, M. M., & Mahmud, M. (2025). Enhancing e-commerce dynamics through deep learning-based customer demand prediction. Transportation Research Procedia, 84, 145–152. https://doi.org/10.1016/j.trpro.2025.03.057

2) Alonso, J. M., Castiello, C., & Mencar, C. (2015). Interpretability of fuzzy systems: Current research trends and prospects. In J. Kacprzyk & W. Pedrycz (eds.), Springer handbook of computational intelligence (pp. 219–237). Springer. https://doi.org/10.1007/978-3-662-43505-2_14

3) Amed, I., Berg, A., Balchandani, A., Hedrich, S., Jensen, J. E., Straub, M., Rölkens, F., Young, R., Brown, P., Le Merle, L., Crump, H., & Dargan, A. (2022). The state of fashion 2022. McKinsey & Company. https://www.mckinsey.com/~/media/mckinsey/industries/retail/our%20insights/state%20of%20fashion/2022/the-state-of-fashion-2022.pdf

4) Arhin, S. A., & Gatiba, A. (2020). Predicting crash injury severity at unsignalized intersections using support vector machines and naïve Bayes classifiers. Transportation Safety and Environment, 2(2), 120–132. https://doi.org/10.1093/tse/tdaa012

5) Arora, A. K., & Yadav, S. (2021). Perception analysis on COVID-19 vaccination: An online cross-sectional study. Prabandhan: Indian Journal of Management, 14(9), 30–43. https://doi.org/10.17010/pijom/2021/v14i9/166295

6) Attaran, M., & Attaran, S. (2019). Opportunities and challenges of implementing predictive analytics for competitive advantage. In S. Miah & W. Yeoh (eds.), Applying business intelligence initiatives in healthcare and organizational settings (pp. 64–90). IGI Global Scientific Publishing. https://doi.org/10.4018/978-1-5225-5718-0.ch004

7) Baig, M. I., Shuib, L., & Yadegaridehkordi, E. (2019). Big data adoption: State of the art and research challenges. Information Processing & Management, 56(6), Article ID 102095. https://doi.org/10.1016/j.ipm.2019.102095

8) Bao, F., Mao, L., Zhu, Y., Xiao, C., & Xu, C. (2022). An improved evaluation methodology for mining association rules. Axioms, 11(1), 17. https://doi.org/10.3390/axioms11010017

9) Barbounaki, S. G., Sarantaki, A., & Gourounti, K. (2021). Fuzzy logic intelligent systems and methods in midwifery and obstetrics. Acta Informatica Medica, 29(3), 210–215. https://doi.org/10.5455/aim.2021.29.210-215

10) ECDB. (2022). Top e-commerce companies: World trends, revenue & strategies. E-commerce Database. https://ecommercedb.com/

11) Eckert, F., Hyndman, R. J., & Panagiotelis, A. (2021). Forecasting Swiss exports using Bayesian forecast reconciliation. European Journal of Operational Research, 291(2), 693–710. https://doi.org/10.1016/j.ejor.2020.09.046

12) Fan, L., Su, H., Wang, W., Zio, E., Zhang, L., Yang, Z., Peng, S., Yu, W., Zuo, L., & Zhang, J. (2022). A systematic method for the optimization of gas supply reliability in natural gas pipeline networks based on Bayesian networks and deep reinforcement learning. Reliability Engineering & System Safety, 225, Article ID 108613. https://doi.org/10.1016/j.ress.2022.108613

13) Fox, R., Goldrick, M., Peng, A., & Whittington, K. (2018). Geek meets chic: Four actions to jump-start advanced analytics in apparel. McKinsey & Company. https://www.mckinsey.com/industries/retail/our-insights/geek-meets-chic-four-actions-to-jump-start-advanced-analytics-in-apparel#/

14) Glantz, M., & Mun, J. (2011). Credit engineering for bankers: A practical guide for bank lending. Academic Press. https://www.sciencedirect.com/book/9780123785855/credit-engineering-for-bankers

15) Gupta, C. P., & Kumar, V. V. (2025). Sentiment analysis: Using different models for monitoring and analyzing customer reviews. Indian Journal of Marketing, 55(5), 8–25. https://doi.org/10.17010/ijom/2025/v55/i5/175017

16) Gurusinghe, R. N., Arachchige, B. J., & Dayarathna, D. (2021). Predictive HR analytics and talent management: A conceptual framework. Journal of Management Analytics, 8(2), 195–221. https://doi.org/10.1080/23270012.2021.1899857

17) Hassan, M. F., Ngah, A. H., & Tio, M. B. (2023). Third-party logistics intention to provide cold transportation services. The mediating effect of top management support and organizational readiness in TOE framework. OPSEARCH, 60, 1603–1625. https://doi.org/10.1007/s12597-023-00683-8

18) Indriasari, E., Soeparno, H., Gaol, F. L., & Matsuo, T. (2019). Application of predictive analytics at financial institutions: A systematic literature review. In 2019 8th International Congress on Advanced Applied Informatics (IIAI-AAI) (pp. 877–883). IEEE. https://doi.org/10.1109/IIAI-AAI.2019.00178

19) Kelleher, J. D., & Tierney, B. (2018). Data science. MIT Press.

20) Khatri, P., Kaushik, N., & Kumari, P. (2022). Positive leadership: Qualitative leadership research using deductive pattern matching approach. Prabandhan: Indian Journal of Management, 15(4), 8–27. https://doi.org/10.17010/pijom/2022/v15i4/169247

21) Kiger, M. E., & Varpio, L. (2020). Thematic analysis of qualitative data: AMEE Guide No. 131. Medical Teacher, 42(8), 846–854. https://doi.org/10.1080/0142159X.2020.1755030

22) Kim, J., Kim, M., Im, S., & Choi, D. (2021). Competitiveness of e-commerce firms through ESG logistics. Sustainability, 13(20), 11548. https://doi.org/10.3390/su132011548

23) Kumar, A., Dash, M., Kumar, A., Hota, S. L., Mohanty, D., & Vasudevan, A. (2024). Path to green prosperity: Evaluating the interconnected factors of industry, finance, and energy in developing economies. Prabandhan: Indian Journal of Management, 17(12), 46–63. https://doi.org/10.17010/pijom/2024/v17i12/174056

24) Kutyłowska, M. (2017). Comparison of two types of artificial neural networks for predicting failure frequency of water conduits. Periodica Polytechnica Civil Engineering, 61(1), 1–6. https://doi.org/10.3311/PPci.8737

25) Lamba, D., Hsu, W. H., & Alsadhan, M. (2021). Chapter 1 - Predictive analytics and machine learning for medical informatics: A survey of tasks and techniques. In Machine learning, big data, and IoT for medical informatics (pp. 1–35). Academic Press. https://doi.org/10.1016/B978-0-12-821777-1.00023-9

26) Latimer, N. R. (2013). Survival analysis for economic evaluations alongside clinical trials—Extrapolation with patient-level data: Inconsistencies, limitations, and a practical guide. Medical Decision Making, 33(6), 743–754. https://doi.org/10.1177/0272989X12472398

27) Liu, L., Zhang, H., Zhou, D., & Shi, J. (2024). Toward fashion intelligence in the big data era: State-of-the-art and future prospects. IEEE Transactions on Consumer Electronics, 70(1), 36–57. https://doi.org/10.1109/TCE.2023.3285880

28) Maroufkhani, P., Tseng, M.-L., Iranmanesh, M., Ismail, W. K., & Khalid, H. (2020). Big data analytics adoption: Determinants and performances among small to medium-sized enterprises. International Journal of Information Management, 54, Article ID 102190. https://doi.org/10.1016/j.ijinfomgt.2020.102190

29) Mileva, G. (2025). Top fashion e-commerce stats, facts, and trends every retailer should be aware of. Influencer Marketing Hub. https://influencermarketinghub.com/fashion-ecommerce-stats/

30) Mohamad, M., Ali, M., Abdullah, A. S., & Elfiky, A. S. (2018). The usage of social media and e-reputation systems in global supply chains: Comparative cases from diamond & automotive industries. International Journal of Communications, Network and System Sciences, 11(5), 69–103. https://doi.org/10.4236/ijcns.2018.115006

31) Mohamad, M., Chowdhury, D., Russell, C., Balikel, A., & Kawalek, P. (2025a). How GenAI transforms computer engineering education: The case of the Middle East and North Africa. Journal of Engineering Education Transformations, 39(SI1), 3–19. https://doi.org/10.16920/jeet/2025/v39is1/25129

32) Mohamad, M., Omeish, F., & Tarabasz, A. (2025b). Metaverse fashion ecosystem: A sustainable marketing perspective. In W. Ozuem, S. Rangfagni & C. Millman (eds.), Sustainable digital marketing for fashion and luxury brands: Theory and practice (pp. 447–473). Palgrave Macmillan. https://doi.org/10.1007/978-3-031-82467-8_19

33) Nagarajan, G., & Babu, D. (2019). Predictive analytics on big data - An overview. Informatica, 43(4), 425 – 459. https://doi.org/10.31449/inf.v43i4.2577

34) Norinder, U., & Norinder, P. (2022). Predicting Amazon customer reviews with deep confidence using deep learning and conformal prediction. Journal of Management Analytics, 9(1), 1–16. https://doi.org/10.1080/23270012.2022.2031324

35) Norouzi, V. (2024). Predicting e-commerce CLV with neural networks: The role of NPS, ATV, and CES. Journal of Economy and Technology, 2, 174–189. https://doi.org/10.1016/j.ject.2024.04.004

36) Otrachshenko, V., Popova, O., Nikolova, M., & Tyurina, E. (2022). COVID-19 and entrepreneurship entry and exit: Opportunity amidst adversity. Technology in Society, 71, Article ID 102093. https://doi.org/10.1016/j.techsoc.2022.102093

37) Pang, W., Ko, J., Kim, S. J., & Ko, E. (2022). Impact of COVID-19 pandemic upon fashion consumer behavior: Focus on mass and luxury products. Asia Pacific Journal of Marketing and Logistics, 34(10), 2149–2164. https://doi.org/10.1108/APJML-03-2021-0189

38) Pappas, I. O., Giannakos, M. N., & Sampson, D. G. (2019). Fuzzy set analysis as a means to understand users of 21st-century learning systems: The case of mobile learning and reflections on learning analytics research. Computers in Human Behavior, 92, 646–659. https://doi.org/10.1016/j.chb.2017.10.010

39) Pasricha, D., Jain, K., & Singh, G. (2020). Antecedents affecting the purchase intention of millennials towards luxury fashion goods: A mixed methods study. Indian Journal of Marketing, 50(1), 24–41. https://doi.org/10.17010/ijom/2020/v50/i1/149772

40) Rogers, E. M. (1962). Diffusion of innovations. Free Press of Glence.

41) Roy, B., Bera, D., Tripathi, P. K., & Upadhyay, S. K. (2021). Improving profitability using predictive analytics. Indian Journal of Marketing, 51(8), 8–25. https://doi.org/10.17010/ijom/2021/v51/i8/165759

42) Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2, 160. https://doi.org/10.1007/s42979-021-00592-x

43) Shastry, K. A., & Shastry, A. (2023). An integrated deep learning and natural language processing approach for continuous remote monitoring in digital health. Decision Analytics Journal, 8, Article ID 100301. https://doi.org/10.1016/j.dajour.2023.100301

44) Statista Research Department. (2025). Retail e-commerce sales worldwide from 2022 to 2028 (in billion U.S. dollars). Statista. https://www.statista.com/statistics/379046/worldwide-retail-e-commerce-sales/

45) Statista. (2022). Global fashion e-commerce market. Statista Database. https://www.statista.com/statistics/1042670/global-top-online-stores-fashion/

46) Sun, Y., Yang, C., Shen, X.-L., & Wang, N. (2020). When digitalized customers meet digitalized services: A digitalized social cognitive perspective of omnichannel service usage. International Journal of Information Management, 54, Article ID 102200. https://doi.org/10.1016/j.ijinfomgt.2020.102200

47) Susto, G. A., Cenedese, A., & Terzi, M. (2018). Chapter 9 - Time-series classification methods: Review and applications to power systems data. In Big data application in power systems (pp. 179–220). Academic Press. https://doi.org/10.1016/B978-0-12-811968-6.00009-7

48) Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books.

49) Tripathi, A., Bagga, T., & Aggarwal, R. K. (2020). Strategic impact of business intelligence: A review of literature. Prabandhan: Indian Journal of Management, 13(3), 35–48. https://doi.org/10.17010/pijom/2020/v13i3/151175

50) Tufail, S., Riggs, H., Tariq, M., & Sarwat, A. I. (2023). Advancements and challenges in machine learning: A comprehensive review of models, libraries, applications, and algorithms. Electronics, 12(8), 1789. https://doi.org/10.3390/electronics12081789

51) Xiao, X., & Tan, B. (2023). Chapter 12: Developing indigenous theory with qualitative IS research. In R. M. Davison (ed.), Handbook of qualitative research methods for information systems: New perspectives (pp. 288–310). Edward Elgar Publishing. https://doi.org/10.4337/9781802205398.00024

52) Zhan, B., Wang, L., & Li, Y. (2021). Precision marketing method of e-commerce platforms based on clustering algorithm. Complexity. https://doi.org/10.1155/2021/5538677

53) Zuo, J. (2021). Analysis of e-commerce characteristics based on edge algorithm and COX model. Wireless Communications and Mobile Computing. https://doi.org/10.1155/2021/6628068